Programming Throwdown

153: ChatGPT

ChatGPT has made a mark on the world as we know it, but that’s only the tip of the AI iceberg. Join us as we discuss how the field of artificial intelligence is growing – including some developments that might not be on your radar!00:00:23 Introductions00:02:01 Jason’s attic adventure00:06:09 Comparing saws00:10:57 Patrick’s surprisingly useful thing00:12:21 SpaceX00:17:31 Human motion diffusion model00:20:47 37Signals00:29:30 Polars00:35:37 Books of the Show00:46:11 Neon00:50:33 Patrick’s player search00:53:47 ChatGPT01:17:12 The threat to Google01:28:06 Jason and Patrick’s future prognostications01:32:13 FarewellsResources mentioned in this episode:Join the Programming Throwdown Patreon community today:  https://www.patreon.com/programmingthrowdown?ty=hNews/Links:SpaceX Starship Static Test Fire Plannedhttps://www.cnet.com/science/space/spacex-prepping-for-first-full-test-fire-of-its-mega-starship-rocket/ Human Motion Diffusion Modelhttps://guytevet.github.io/mdm-page/ 37Signals Leaving the Cloud and Details Cloud Costshttps://twitter.com/dhh/status/1613508201953038337 Polars: DataFrames in Rusthttps://docs.rs/polars/latest/polars/index.html Book of the Show:Jason: Build by Tony Fadellhttps://amzn.to/3wpLnLW Patrick: Age of Myth by Michael Sullivan (Riyria)https://amzn.to/3HlEsJ5 Tool of the Show:Jason: Neon: Serverless Postgres: https://neon.tech/ Patrick: 7 Billion Humans (Steam): https://store.steampowered.com/app/792100/7_Billion_Humans/ If you’ve enjoyed this episode, you can listen to more on Programming Throwdown’s website: https://www.programmingthrowdown.com/Reach out to us via email: programmingthrowdown@gmail.comYou can also follow Programming Throwdown on Facebook | Apple Podcasts | Spotify | Player.FM Join the discussion on our DiscordHelp support Programming Throwdown through our Patreon ★ Support this podcast on Patreon ★

152: The Future Database with Sam Lambert

Databases are key to almost any project, large or small.  Most database systems in the cloud are designed for heavy use and the costs can get expensive quickly, but database-as-a-service is a rapidly growing area, where many databases can share the same hardware for a much reduced rate, or even for free!  Sam Lambert, CEO of PlanetScale, joins Jason and Patrick to discuss database-as-a-service.00:01:41 Introductions00:02:34 Sam’s Github learning lesson00:07:08 The day after00:10:57 Getting started with databases00:14:21 Schema change difficulties00:19:47 Database transactions00:31:15 Why data recovery matters00:38:35 Planetscale00:49:24 Greetings from the past01:02:01 How Jason discovered Planetscale01:06:53 Branching01:14:00 The vision for Planetscale01:18:12 The rationale behind Planetscale’s work setup01:24:29 Careers at Planetscale01:28:06 Amp It Up01:33:10 FarewellsResources mentioned in this episode:Links:Sam Lambert:Linkedin: https://www.linkedin.com/in/isamlambert/ PlanetScale:Website: https://planetscale.com/ Twitter: https://twitter.com/planetscaledata Linkedin: https://www.linkedin.com/company/planetscale/ Github: https://github.com/planetscale Careers: https://planetscale.com/careers Amp It Up (Amazon):Paperback: https://www.amazon.com/Amp-Unlocking-Hypergrowth-Expectations-Intensity/dp/1119836115 Audiobook: https://www.amazon.com/Amp-Hypergrowth-Expectations-Increasing-Elevating/dp/B09QBRBKFB/ If you’ve enjoyed this episode, you can listen to more on Programming Throwdown’s website: https://www.programmingthrowdown.com/Reach out to us via email: programmingthrowdown@gmail.com You can also follow Programming Throwdown on Facebook | Apple Podcasts | Spotify | Player.FM  Join the discussion on our DiscordHelp support Programming Throwdown through our Patreon ★ Support this podcast on Patreon ★

151: Machine Learning Engineering with Liran Hason

Machine Learning Engineer is one of the fastest growing professions on the planet.  Liran Hason, co-founder and CEO of Aporia, joins us to discuss this new field and how folks can learn the skills and gain the experience needed to become an ML Engineer!00:00:59 Introductions00:01:44 How Liran got started making websites00:07:03 College advice for getting involved in real-world experience00:12:51 Jumping into the unknown00:15:22 ML engineering00:20:50 The missing part in data science development00:29:16 How to build skills in the ML space00:37:01 A horror story00:41:34 Model loading questions00:47:36 Must-have skills in an ML resume00:50:41 Deciding about data science00:59:08 Rust01:06:27 How Aporia contributes to the data science space01:14:26 Working at Aporia01:16:53 FarewellsResources mentioned in this episode:Links:Liran Hason:Linkedin: https://www.linkedin.com/in/hasuni/ Aporia:Website: https://www.aporia.com/ Twitter: https://twitter.com/aporiaai Linkedin: https://www.linkedin.com/company/aporiaai/ Github: https://github.com/aporia-ai The Mom Test (Amazon):Paperback: https://www.amazon.com/Mom-Test-customers-business-everyone/dp/1492180742 Audiobook: https://www.amazon.com/The-Mom-Test-Rob-Fitzpatrick-audiobook/dp/B07RJZKZ7F References:Shadow Mode: https://christophergs.com/machine%20learning/2019/03/30/deploying-machine-learning-applications-in-shadow-mode/ Blue-green deployment: https://en.wikipedia.org/wiki/Blue-green_deployment Coursera ML Specialization (Stanford): https://www.coursera.org/specializations/machine-learning-introduction Auto-retraining: https://neptune.ai/blog/retraining-model-during-deployment-continuous-training-continuous-testing If you’ve enjoyed this episode, you can listen to more on Programming Throwdown’s website: https://www.programmingthrowdown.com/Reach out to us via email: programmingthrowdown@gmail.comYou can also follow Programming Throwdown on Facebook | Apple Podcasts | Spotify | Player.FM Join the discussion on our DiscordHelp support Programming Throwdown through our Patreon ★ Support this podcast on Patreon ★

150: Code Reviews with On Freund

Patrick and I are always stressing the importance of code reviews and collaboration when developing.  On Freund, co-founder & CEO at Wilco, is super familiar with how code review processes can go well, or become a hinderance. In today’s episode with us, he shares his unique perspective on code reviews and maintaining high code quality!00:00:56 Introductions00:01:38 On’s first exposure to tech00:06:04 Game development adventures00:11:12 The difference between university and real-world experiences00:17:43 A context switch question00:24:41 Points of frustration00:30:53 Build versus Buy complications00:32:06 Code reviews00:39:58 Quality of code00:45:12 Using callouts for the right reasons00:49:57 Code reviews can be too late sometimes00:52:11 Using social interaction as pre-review orientation00:57:03 How not to use code reviews01:01:35 Where Wilco helps programmers learn01:09:11 Working in Wilco01:11:49 FarewellsResources mentioned in this episode:Links:On Freund:Linkedin: https://www.linkedin.com/in/onfreund Wilco:Website: https://www.trywilco.com/ Twitter: https://twitter.com/trywilco Linkedin: https://www.linkedin.com/company/trywilco  References:Micro-Adventure:https://en.wikipedia.org/wiki/Micro_Adventure If you’ve enjoyed this episode, you can listen to more on Programming Throwdown’s website: https://www.programmingthrowdown.com/ Reach out to us via email: programmingthrowdown@gmail.com You can also follow Programming Throwdown on Facebook | Apple Podcasts | Spotify | Player.FM Join the discussion on our DiscordHelp support Programming Throwdown through our Patreon ★ Support this podcast on Patreon ★

149: Workflow Engines with Sanjay Siddhanti

At scale, anything we build is going to involve people.  Many of us have personal schedules and to-do lists, but how can we scale that to hundreds or even thousands of people?  When you file a help ticket at a massive company like Google or Facebook, ever wonder how that ticket is processed? Sanjay Siddhanti, Akasa’s Director of Engineering, is no slouch when it comes to navigating massive workflow engines – and in today’s episode, he shares his experiences in bioinformatics, workflows, and more with us.00:00:39 Workflow engine definitions00:01:40 Introductions00:02:24 Sanjay’s 8th grade programming experience00:05:28 Bioinformatics00:10:29 The academics-vs-industry dilemma00:16:52 Small company challenges00:18:18 Correctly identifying when to scale00:24:04 The solution Akasa provides00:31:38 Workflow engines in detail00:36:02 ETL frameworks00:45:06 The intent of integration construction00:47:13 Delivering a platform vs delivering a solution00:50:04 Working within US medico-legal frameworks00:53:28 Inadvertent uses of API calls00:55:47 Working in Akasa00:57:09 Interning in Akasa00:58:35 FarewellsResources mentioned in this episode:Sanjay:Twitter: https://twitter.com/siddhantis Linkedin: https://www.linkedin.com/in/sanjaysiddhanti/ Akasa:Website: https://www.akasa.com Sanjay’s Q&A https://akasa.com/blog/10-questions-for-sanjay-siddhanti-director-of-engineering-at-akasa/ Careers: https://akasa.com/careers/ Interning: https://www.linkedin.com/jobs/view/research-intern-ai-spring-summer-2023-at-akasa-3206403183/ References:Episode 33: Design Patterns:https://www.programmingthrowdown.com/2014/05/episode-33-design-patterns.html The Mythical Man-Month:https://en.wikipedia.org/wiki/The_Mythical_Man-Month If you’ve enjoyed this episode, you can listen to more on Programming Throwdown’s website: https://www.programmingthrowdown.com/Reach out to us via email: programmingthrowdown@gmail.comYou can also follow Programming Throwdown on Facebook | Apple Podcasts | Spotify | Player.FM Join the discussion on our DiscordHelp support Programming Throwdown through our Patreon ★ Support this podcast on Patreon ★